Toward RoboCup without Color Labeling

Towards RoboCup without Color Labeling

Towards Eliminating Manual Color Calibration at RoboCup

Color calibration is a time-consuming, and therefore costly requirement for most robot teams at RoboCup. This paper presents an approach for autonomous color learning on-board a mobile robot with limited computational and memory resources. It works without any labeled training data and trains autonomously from a color-coded map of its environment. The process is fully implemented, completely au...

Getting the Most from Your Color Camera in a Color-Coded World

In this paper we present a proposal for setting camera parameters which we claim to give results better matched to applications in color-coded environments then the camera internal algorithms. Moreover it does not require online human intervention, i.e. is automated, and is faster than a human operator. This work applies to situations where the camera is used to extract information from a color...

Real-time object tracking for soccer-robots without color information

Objects in the RoboCup scenario (soccer playing robots) are identified by their unique color. These visual cues will be removed in the near future so that new vision algorithms are needed to cope with this. In this paper we present a method for detecting and tracking the ball in a RoboCup scenario without the need for color information. We use Haar-like features trained by an adaboost algorithm...

The Vision System for Robocup Small Robot League

The Robot World Cup Initiative (RoboCup) is an international joint project to promote AI, robotics, and related field [1]. It provides a standard platform for robotic soccer game. The aim of the vision system for RoboCup small robot league is to track and predict the motion states of 10 agents and a ball, and send the states information to the Artificial Intelligence module. This paper presents...